用强化学习自动探测芯片漏洞，省去人工手动分析。
Towards Reinforcement Learning for Exploration of Speculative Execution Vulnerabilities
- 用强化学习智能探索芯片漏洞
- 可发现黑盒芯片中的推测执行泄漏
- 适合安全研究者和硬件验证工程师
推测攻击（如Spectre）可在操作系统未察觉的情况下泄露机密信息。推测执行漏洞难以发现且深度嵌入硬件，传统利用需大量人工与硬件知识。本文提出SpecRL框架，采用强化学习在后硅阶段（黑盒）微处理器中自动搜索推测执行漏洞。
原文摘要 · Abstract (English)
Speculative attacks such as Spectre can leak secret information without being discovered by the operating system. Speculative execution vulnerabilities are finicky and deep in the sense that to exploit them, it requires intensive manual labor and intimate knowledge of the hardware. In this paper, we introduce SpecRL, a framework that utilizes reinforcement learning to find speculative execution leaks in post-silicon (black box) microprocessors.
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